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AI モデル 84759
- sac-BipedalWalkerHardcore-v3↗
sb3
This is a trained model of a **SAC** agent playing **BipedalWalkerHardcore-v3** using the stable-baselines3 library and the RL Zoo. The RL Zoo is a training framework for Stable Baselines3 reinforcement learning agents, with hyperparameter optimization and pre-trained agents included. RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo SB3: https://github.com/DLR-RM/stable-baselines3 SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
- Llama-3.3-70B-Narrative-Deconcentration-q8_0.gguf↗
schonsense
- nli-distilbert-base-max-pooling↗
sentence-transformers
- GLM-5.3-ewaste-edition-GGUF↗
SixVolts
- mamba2-370m↗
state-spaces
- sn-xlm-roberta-base-snli-mnli-anli-xnli↗
symanto
- flint-GGUF↗
terelabs
- gte-large↗
thenlper
General Text Embeddings (GTE) model. Towards General Text Embeddings with Multi-stage Contrastive Learning The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including GTE-large, GTE-base, and GTE-small. The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc. We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the MTEB leaderboard.
- MusicFM↗
tky823
- embeddinggemma-300m↗
unsloth
**Model Page**: EmbeddingGemma * Responsible Generative AI Toolkit * EmbeddingGemma on Kaggle * EmbeddingGemma on Vertex Model Garden EmbeddingGemma is a 300M parameter, state-of-the-art for its size, open embedding model from Google, built from Gemma 3 (with T5Gemma initialization) and the same research and technology used to create Gemini models. EmbeddingGemma produces vector representations of text, making it well-suited for search and retrieval tasks, including classification, clustering, and semantic similarity search. This model was trained with data in 100+ spoken languages.